Search ga_ls7e_ard_3 across November 2018

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud pixels. For these ARD products, this value comes from the Fmask algorithm. to (exclusive):

Time when dataset was created (processed) From

Time when dataset was created (processed) to (exclusive)

The raw CRS string as it appears in metadata:

One of - final|interim|nrt (near real time):

ID of a dataset type:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. from:

Ground sampling distance of the sensor’s best resolution band in metres; represents the size (or spatial resolution) of one pixel. to (exclusive):

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north from:

The azimuth angle of the sun at the moment of acquisition, in degree units measured clockwise from due north to (exclusive):

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. from:

The elevation angle of the sun at the moment of acquisition, in degree units relative to the horizon. to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear land pixels according to the Fmask algorithm to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains cloud shadow pixels according to the Fmask algorithm to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear snow pixels according to the Fmask algorithm to (exclusive):

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm from:

The proportion (from 0 to 100) of the dataset's valid data area that contains clear water pixels according to the Fmask algorithm to (exclusive):

File format (GeoTiff, NetCDF):

GQA Circular error probable (90%) from:

GQA Circular error probable (90%) to (exclusive):

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the absolute values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the absolute values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the absolute values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Absolute value of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Absolute value of the total GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Absolute value of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Circular error probable (90%) of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals after removal of outliers, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Mean of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the x-axis (east-to-west) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) from:

Standard Deviation of the values of the y-axis (north-to-south) GCP residuals, in pixel units based on a 25 metre resolution reference image (i.e. 0.2 = 5 metres) to (exclusive):

Dataset UUID:

User who indexed the dataset:

When dataset was indexed:

Label:

A Landsat Collection 1 Level-1 product identifier including the Collection processing levels, processing date, collection number, and collection tier category (e.g. ‘LE07_L1TP_098084_20210307_20210308_02_RT’) :

A Landsat scene ID including WRS path and row, year and day, ground station identifier and archive version number (e.g. ‘LC80900852021066LGN00’) :

Latitude range from:

Latitude range to (exclusive):

Longitude range from:

Longitude range to (exclusive):

Full metadata document:

Metadata type name of dataset:

ID of a metadata type:

Spatial reference code from the provider. For Landsat region_code is a scene path row: '{:03d}{:03d}.format(path,row)' For Sentinel it is MGRS code. In general it is a unique string identifier that datasets covering roughly the same spatial region share. :

Acquisition time range From

Acquisition time range to (exclusive)

Dataset URI:

Add field

First 100 results (query limit)

Time (local)	Label
2018-11-01 09:25:54	ga_ls7e_ard_3-0-0_092075_2018-10-31_final
2018-11-01 09:27:05	ga_ls7e_ard_3-0-0_092078_2018-10-31_final
2018-11-01 09:27:29	ga_ls7e_ard_3-0-0_092079_2018-10-31_final
2018-11-01 09:27:53	ga_ls7e_ard_3-0-0_092080_2018-10-31_final
2018-11-01 09:28:17	ga_ls7e_ard_3-0-0_092081_2018-10-31_final
2018-11-01 09:28:41	ga_ls7e_ard_3-0-0_092082_2018-10-31_final
2018-11-01 09:29:05	ga_ls7e_ard_3-0-0_092083_2018-10-31_final
2018-11-01 09:29:29	ga_ls7e_ard_3-0-0_092084_2018-10-31_final
2018-11-01 09:29:53	ga_ls7e_ard_3-1-0_092085_2018-10-31_final
2018-11-01 09:30:17	ga_ls7e_ard_3-0-0_092086_2018-11-01_final
2018-11-14 10:30:47	ga_ls7e_ard_3-0-0_103068_2018-11-14_final
2018-11-14 10:31:11	ga_ls7e_ard_3-0-0_103069_2018-11-14_final
2018-11-14 10:31:35	ga_ls7e_ard_3-0-0_103070_2018-11-14_final
2018-11-14 10:31:59	ga_ls7e_ard_3-0-0_103071_2018-11-14_final
2018-11-14 10:32:47	ga_ls7e_ard_3-0-0_103073_2018-11-14_final
2018-11-14 10:33:11	ga_ls7e_ard_3-0-0_103074_2018-11-14_final
2018-11-14 10:33:35	ga_ls7e_ard_3-0-0_103075_2018-11-14_final
2018-11-14 10:33:59	ga_ls7e_ard_3-0-0_103076_2018-11-14_final
2018-11-14 10:34:23	ga_ls7e_ard_3-0-0_103077_2018-11-14_final
2018-11-14 10:34:46	ga_ls7e_ard_3-0-0_103078_2018-11-14_final
2018-11-14 10:35:10	ga_ls7e_ard_3-0-0_103079_2018-11-14_final
2018-11-14 10:35:34	ga_ls7e_ard_3-0-0_103080_2018-11-14_final
2018-11-14 10:35:58	ga_ls7e_ard_3-0-0_103081_2018-11-14_final
2018-11-15 09:34:44	ga_ls7e_ard_3-1-0_094067_2018-11-15_final
2018-11-15 09:37:08	ga_ls7e_ard_3-0-0_094073_2018-11-15_final
2018-11-15 09:37:32	ga_ls7e_ard_3-0-0_094074_2018-11-15_final
2018-11-15 09:37:56	ga_ls7e_ard_3-0-0_094075_2018-11-15_final
2018-11-15 09:38:20	ga_ls7e_ard_3-0-0_094076_2018-11-15_final
2018-11-15 09:38:44	ga_ls7e_ard_3-0-0_094077_2018-11-15_final
2018-11-15 09:39:08	ga_ls7e_ard_3-0-0_094078_2018-11-15_final
2018-11-15 09:39:32	ga_ls7e_ard_3-0-0_094079_2018-11-15_final
2018-11-15 09:39:56	ga_ls7e_ard_3-0-0_094080_2018-11-15_final
2018-11-15 09:40:20	ga_ls7e_ard_3-0-0_094081_2018-11-15_final
2018-11-15 09:40:44	ga_ls7e_ard_3-0-0_094082_2018-11-15_final
2018-11-15 09:41:07	ga_ls7e_ard_3-1-0_094083_2018-11-15_final
2018-11-15 09:41:31	ga_ls7e_ard_3-0-0_094084_2018-11-15_final
2018-11-15 09:41:55	ga_ls7e_ard_3-0-0_094085_2018-11-15_final
2018-11-15 09:42:19	ga_ls7e_ard_3-0-0_094086_2018-11-15_final
2018-11-15 09:42:43	ga_ls7e_ard_3-0-0_094087_2018-11-15_final
2018-11-15 11:13:37	ga_ls7e_ard_3-0-0_110067_2018-11-15_final
2018-11-15 11:15:13	ga_ls7e_ard_3-0-0_110071_2018-11-15_final
2018-11-15 11:15:37	ga_ls7e_ard_3-1-0_110072_2018-11-15_final
2018-11-15 11:16:01	ga_ls7e_ard_3-0-0_110073_2018-11-15_final
2018-11-15 11:16:25	ga_ls7e_ard_3-0-0_110074_2018-11-15_final
2018-11-15 11:16:49	ga_ls7e_ard_3-1-0_110075_2018-11-15_final
2018-11-15 11:17:13	ga_ls7e_ard_3-0-0_110076_2018-11-15_final
2018-11-15 11:17:37	ga_ls7e_ard_3-0-0_110077_2018-11-15_final
2018-11-15 11:18:01	ga_ls7e_ard_3-0-0_110078_2018-11-15_final
2018-11-15 11:18:25	ga_ls7e_ard_3-0-0_110079_2018-11-15_final
2018-11-15 11:18:49	ga_ls7e_ard_3-0-0_110080_2018-11-15_final
2018-11-15 11:19:13	ga_ls7e_ard_3-0-0_110081_2018-11-15_final
2018-11-15 11:19:36	ga_ls7e_ard_3-0-0_110082_2018-11-15_final
2018-11-15 11:20:00	ga_ls7e_ard_3-0-0_110083_2018-11-15_final
2018-11-15 11:20:24	ga_ls7e_ard_3-0-0_110084_2018-11-15_final
2018-11-16 10:19:34	ga_ls7e_ard_3-0-0_101071_2018-11-16_final
2018-11-16 10:19:58	ga_ls7e_ard_3-0-0_101072_2018-11-16_final
2018-11-16 10:20:22	ga_ls7e_ard_3-0-0_101073_2018-11-16_final
2018-11-16 10:20:46	ga_ls7e_ard_3-0-0_101074_2018-11-16_final
2018-11-16 10:21:10	ga_ls7e_ard_3-1-0_101075_2018-11-16_final
2018-11-16 10:21:34	ga_ls7e_ard_3-0-0_101076_2018-11-16_final
2018-11-16 10:21:58	ga_ls7e_ard_3-0-0_101077_2018-11-16_final
2018-11-16 10:22:22	ga_ls7e_ard_3-0-0_101078_2018-11-16_final
2018-11-16 10:22:46	ga_ls7e_ard_3-0-0_101079_2018-11-16_final
2018-11-16 10:23:10	ga_ls7e_ard_3-0-0_101080_2018-11-16_final
2018-11-16 10:23:34	ga_ls7e_ard_3-0-0_101081_2018-11-16_final
2018-11-16 10:23:58	ga_ls7e_ard_3-0-0_101082_2018-11-16_final
2018-11-16 10:24:22	ga_ls7e_ard_3-0-0_101083_2018-11-16_final
2018-11-17 09:25:31	ga_ls7e_ard_3-0-0_092075_2018-11-16_final
2018-11-17 09:25:55	ga_ls7e_ard_3-0-0_092076_2018-11-16_final
2018-11-17 09:26:19	ga_ls7e_ard_3-0-0_092077_2018-11-16_final
2018-11-17 09:26:43	ga_ls7e_ard_3-0-0_092078_2018-11-16_final
2018-11-17 09:27:07	ga_ls7e_ard_3-0-0_092079_2018-11-16_final
2018-11-17 09:27:31	ga_ls7e_ard_3-0-0_092080_2018-11-16_final
2018-11-17 09:27:55	ga_ls7e_ard_3-0-0_092081_2018-11-16_final
2018-11-17 09:28:19	ga_ls7e_ard_3-0-0_092082_2018-11-16_final
2018-11-17 09:28:43	ga_ls7e_ard_3-0-0_092083_2018-11-16_final
2018-11-17 09:29:07	ga_ls7e_ard_3-0-0_092084_2018-11-16_final
2018-11-17 09:29:31	ga_ls7e_ard_3-0-0_092085_2018-11-16_final
2018-11-17 09:30:19	ga_ls7e_ard_3-0-0_092087_2018-11-17_final
2018-11-17 09:30:43	ga_ls7e_ard_3-0-0_092088_2018-11-17_final
2018-11-17 09:31:07	ga_ls7e_ard_3-0-0_092089_2018-11-17_final
2018-11-17 11:02:25	ga_ls7e_ard_3-0-0_108070_2018-11-17_final
2018-11-17 11:02:49	ga_ls7e_ard_3-1-0_108071_2018-11-17_final
2018-11-17 11:03:13	ga_ls7e_ard_3-0-0_108072_2018-11-17_final
2018-11-17 11:03:36	ga_ls7e_ard_3-0-0_108073_2018-11-17_final
2018-11-17 11:04:00	ga_ls7e_ard_3-0-0_108074_2018-11-17_final
2018-11-17 11:04:24	ga_ls7e_ard_3-1-0_108075_2018-11-17_final
2018-11-17 11:04:48	ga_ls7e_ard_3-0-0_108076_2018-11-17_final
2018-11-17 11:05:12	ga_ls7e_ard_3-0-0_108077_2018-11-17_final
2018-11-17 11:05:36	ga_ls7e_ard_3-0-0_108078_2018-11-17_final
2018-11-17 11:06:00	ga_ls7e_ard_3-0-0_108079_2018-11-17_final
2018-11-17 11:06:24	ga_ls7e_ard_3-0-0_108080_2018-11-17_final
2018-11-17 11:06:48	ga_ls7e_ard_3-0-0_108081_2018-11-17_final
2018-11-17 11:07:12	ga_ls7e_ard_3-0-0_108082_2018-11-17_final
2018-11-17 11:07:36	ga_ls7e_ard_3-0-0_108083_2018-11-17_final
2018-11-17 11:08:00	ga_ls7e_ard_3-0-0_108084_2018-11-17_final
2018-11-19 09:13:55	ga_ls7e_ard_3-0-0_090077_2018-11-18_final
2018-11-19 09:14:19	ga_ls7e_ard_3-0-0_090078_2018-11-18_final
2018-11-19 09:14:43	ga_ls7e_ard_3-0-0_090079_2018-11-18_final
2018-11-19 09:15:07	ga_ls7e_ard_3-0-0_090080_2018-11-18_final

Return to ga_ls7e_ard_3 across November 2018 overview

ga_ls7e_ard_3: Geoscience Australia Landsat 7 Enhanced Thematic Mapper Plus Analysis Ready Data Collection 3

Baseline satellite data

Inland water

Land and vegetation

Sea, ocean and coast

Hazards

Other products

2018

November

All days